Exhaust emission prediction system and method

ABSTRACT

An exhaust emission prediction system includes an engine configured to generate a flow of exhaust and a controller configured to determine a first estimation of an amount of an emissions constituent at a first location using an empirical model. The first location is downstream of the engine. The controller is also configured to determine a second estimation of the amount of the emissions constituent at the first location using a physics-based model and determine a third estimation of the amount of the emissions constituent at the first location based on at least one of the first estimation or the second estimation.

TECHNICAL FIELD

The present disclosure relates generally to an exhaust system, and moreparticularly, to an exhaust emission prediction system and method.

BACKGROUND

Internal combustion engines, including diesel engines, gasoline engines,gaseous fuel-powered engines, and other engines known in the art, mayproduce a flow of exhaust composed of gaseous and solid compounds,including particulate matter, nitrogen oxides (NOx), and sulfurcompounds. Due to heightened environmental concerns, exhaust emissionstandards have become increasingly stringent. The amount of one or moreconstituents of the flow of exhaust emitted from the engine may beregulated depending on the type, size, and/or class of engine.

One method that has been implemented by engine manufacturers to complywith the regulation of NOx exhausted to the environment is a strategycalled selective catalytic reduction (SCR). SCR is a process by whichgaseous or liquid reductant (e.g., a mixture of urea and water) isinjected into the flow of exhaust from the engine. The combined flow mayform ammonia (NH₃), which may then be absorbed onto an SCR catalyst. Theammonia may react with NOx in the flow of exhaust to form H₂O and N₂,thereby reducing the amount of NOx in the flow of exhaust.

The ability of the SCR catalyst to reduce NOx depends upon many factors,such as catalyst formulation, the size of the SCR catalyst, exhaust gastemperature, and urea dosing rate. With regard to the dosing rate, theNOx reduction efficiency tends to increase linearly until the dosingrate reaches a certain limit. Above the limit, the NOx reductionefficiency may increase at a slower rate because the ammonia may besupplied at a faster rate than the NOx reduction process can consume.The excess ammonia, known as ammonia slip, may be expelled from the SCRcatalyst.

The urea dosing rate may be controlled using signals from a NOx sensingdevice, such as a NOx sensor, placed in the exhaust stream after the SCRcatalyst. The NOx sensing device may measure the level of NOx andprovide signals to a SCR controller to adjust the urea dosing rate.Although NOx reduction efficiency may be increased using this process,the costs and maintenance associated with NOx sensing devices may makeimplementing this process unattractive to engine manufacturers.

To minimize the costs associated with physical sensors, someconventional engine control systems may implement virtual sensors. Forexample, U.S. Pat. No. 6,236,908 issued to Cheng et al. (the '908patent) describes an engine control module (ECM) including one or moreneural networks that act as virtual sensing devices to replace orenhance traditional physical sensors. The ECM receives values associatedwith various engine operating parameters from a plurality of physicalsensors and applies the values to the neural network to produce valuesfor one or more output parameters. For example, the neural network mayreceive values, such as engine speed, manifold pressure, exhaust gasrecirculation, and air/flow ratio values, from physical sensors. Basedon the input values, the neural network may determine values of otherengine operating parameters, including residual mass fraction,emissions, knock index, peak pressure rise rate, exhaust gastemperature, and exhaust gas oxygen content. The neural network istrained using data produced by a simulation model calibrated with actualengine test data.

Although the '908 patent suggests the ability to use a neural network asa virtual sensing device to replace or enhance traditional physicalsensors, the available engine test data used to train the neural networkis limited. The '908 patent describes that the simulation modelinterpolates or extrapolates a more complete set of data. However, theinterpolated or extrapolated data may not be accurate, which may causethe neural network to provide inaccurate outputs.

The disclosed system is directed to overcoming one or more of theproblems set forth above.

SUMMARY

In one aspect, the present disclosure is directed to an exhaust emissionprediction system. The exhaust emission prediction system includes anengine configured to generate a flow of exhaust and a controllerconfigured to determine a first estimation of an amount of an emissionsconstituent at a first location using an empirical model. The firstlocation is downstream of the engine. The controller is also configuredto determine a second estimation of the amount of the emissionsconstituent at the first location using a physics-based model anddetermine a third estimation of the amount of the emissions constituentat the first location based on at least one of the first estimation orthe second estimation.

In another aspect, the present disclosure is directed to a method ofpredicting an amount of NOx in a flow of exhaust from an engine using acontroller. The method includes determining, using the controller, afirst estimation of the amount of NOx at a first location using anempirical model, the first location being downstream of the engine. Themethod also includes determining, using the controller, a secondestimation of the amount of NOx at the first location using aphysics-based model, and determining, using the controller, a thirdestimation of the amount of NOx at the first location based on at leastone of the first estimation or the second estimation.

In another aspect, the present disclosure is directed to an enginesystem including an engine configured to generate a flow of exhaust, aninjector configured to inject a reductant into the flow of exhaust, anda catalytic device configured to receive the flow of exhaust after beinginjected with the reductant. The engine system also includes a processorand a memory module configured to store instructions that, whenexecuted, enable the processor to determine a first estimation of anamount of NOx at a first location using an empirical model. The firstlocation is downstream of the engine and upstream of the catalyticdevice. The memory module is also configured to store instructions that,when executed, enable the processor to determine a second estimation ofthe amount of NOx at the first location using a physics-based model,determine a third estimation of the amount of NOx at the first locationbased on at least one of the first estimation or the second estimation,and adjust an amount of the reductant injected by the injector based onthe determined third estimation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic illustration of an engine and an exhaustemission prediction system, according to an exemplary embodiment;

FIG. 2 is a diagrammatic illustration of a controller for the exhaustemission prediction system of FIG. 1;

FIG. 3 is a diagrammatic illustration of an empirical NOx model for thecontroller of FIG. 2;

FIG. 4 is a diagrammatic illustration of a physics-based NOx model forthe controller of FIG. 2; and

FIG. 5 is a flow chart illustrating an exemplary disclosed method ofestimating an amount of an emissions constituent in a flow of exhaust,according to an exemplary embodiment.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments, which areillustrated in the accompanying drawings. Wherever possible, the samereference numbers will be used throughout the drawings to refer to thesame or like parts.

FIG. 1 is a diagrammatic illustration of a power source, such as anengine 10, of a machine and an exhaust emission prediction system,according to an exemplary embodiment. The disclosed embodiment may beapplicable to various types of machines such as, for example, a fixed ormobile machine that performs some type of operation associated with anindustry such as mining, construction, farming, transportation, powergeneration, tree harvesting, forestry, or any other industry known inthe art. The engine 10 may be an internal combustion engine, such as,for example, a diesel engine, a gasoline engine, a gaseous fuel-poweredengine, or any other engine apparent to one skilled in the art. Theengine 10 may alternatively be another source of power such as a furnaceor any other suitable source of power for a powered system such as afactory or power plant.

Operation of the engine 10 may produce power and a flow of exhaust. Forexample, the engine 10 may include a plurality of cylinders 12. Eachcylinder 12 may include a combustion chamber that may mix fuel with airand/or recirculated exhaust gas, as described below, and burn themixture therein to produce the flow of exhaust. The flow of exhaust maycontain carbon monoxide, NOx, carbon dioxide, aldehydes, soot, oxygen,nitrogen, water vapor, and/or hydrocarbons.

An exhaust system 14 is provided with the engine 10 such that the flowof exhaust may be fluidly communicated from the engine 10 to the exhaustsystem 14. The flow of exhaust produced by the engine 10 may be directedfrom the engine 10 to components of the exhaust system 14 by flow lines.For example, as shown in FIG. 1, the flow lines may include pipes,tubing, conduits, and/or other exhaust-carrying structures known in theart through which the flow of exhaust may be directed through theexhaust system 14 to one or more of a turbine 16 of a turbocharger 18,one or more aftertreatment devices 20, an injector 22, and a catalyticdevice (e.g., an SCR catalyst 24) in the exhaust system 14. The exhaustsystem 14 may also include additional components for directing the flowof exhaust out of the engine 10 that are known in the art.

The turbine 16 may be disposed between an exhaust passageway of theengine 10 and the inlet of the exhaust system 14. The turbine 16 may beconfigured to drive a connected compressor 26 of the turbocharger 18.For example, as the hot exhaust gases exiting the engine 10 expandagainst blades (not shown) of the turbine 16, the turbine 16 may rotateand drive the compressor 26. The compressor 26 may be located in an airinduction system of the engine 10 and may be configured to compress theatmospheric air received by the air induction system to a predeterminedpressure level. The air induction system may also include additionalcomponents for introducing the compressed air into the cylinders 12 ofthe engine 10, such as, for example, a filter, a valve, air cleaner, aircooler, waste gate, a venturi, etc., as known in the art.

The aftertreatment device(s) 20 may be configured to remove particulatesand other constituents from the flow of exhaust, e.g., a filter forcapturing particulates, ash, or other materials from the exhaust gas toprevent their discharge into the surrounding environment, such as adiesel particulate filter (DPF), a system for regenerating the filter byremoving the particulate matter trapped by the filter, other catalyticdevices, and/or other exhaust gas treatment devices. For example, adiesel oxidation catalyst (DOC) may raise the NO₂/NOx ratio, which mayimprove the NOx conversion efficiency of the SCR catalyst 24. One ormore aftertreatment device(s) may also be located downstream of the SCRcatalyst 24, such as an ammonia oxidation (AMOX) catalyst that mayoxidize ammonia that slips from the SCR catalyst 24 to form N₂ and H₂O.

The injector 22 may be connected to a reductant supply (not shown) andmay inject reductant, such as urea, urea and water, ammonia, and/orother elements or compounds capable of chemically reducing compounds,e.g., NOx, contained within the flow of exhaust in the presence of, forexample, catalyst materials. The injector 22 may include a nozzle (notshown) or other flow control device configured to assist in controllablyreleasing a flow of the reductant into the flow of exhaust from theengine 10. The nozzle may be any type of injector known in the art andmay include any device capable of injecting and/or atomizing an injectedfluid.

The SCR catalyst 24 may chemically reduce the amount of NOx in the flowof exhaust. The reductant injected into the flow of exhaust by theinjector 22 upstream from the SCR catalyst 24 may be absorbed onto theSCR catalyst 24 so that the reductant may react with NOx in the flow ofexhaust to form H₂O (water vapor) and N₂ (nitrogen gas). For example, amixture of urea and water injected by the injector 22 may decompose toammonia, and the SCR catalyst 24 may facilitate a reaction between theammonia and NOx in the flow of exhaust to produce water and nitrogengas, thereby removing NOx from the flow of exhaust. The SCR catalyst 24may include catalyst materials such as, but not limited to, zeolites(e.g., iron zeolite or copper zeolite) or vanadia.

A portion of the flow of exhaust exiting the SCR catalyst 24 may enteran exhaust gas recirculation (EGR) passageway 28, which may direct aflow of recirculated exhaust to the compressor 26 for subsequentcombustion while the remaining portion of the flow of exhaust exitingthe SCR catalyst 24 may be output from the exhaust system 14, e.g.,released into the surrounding atmosphere, such as through a tail pipe.Alternatively, the EGR passageway 28 may be configured to direct a flowof recirculated exhaust exiting at least one of the aftertreatmentdevice(s) 20 upstream from the injector 22 to the compressor 26 whilethe remaining portion of the flow of exhaust exiting at least one of theaftertreatment device(s) 20 may be directed to the injector 22 and theSCR catalyst 24 before being output from the exhaust system 14.

The engine 10 may also be provided with an intake manifold 11 and/or anexhaust manifold. The intake manifold 11 may receive the compressed airand/or recirculated exhaust, and allow the compressed air and/orrecirculated exhaust to flow to the cylinders 12. The exhaust manifoldmay receive the flow of exhaust from the cylinders 12 and direct theflow of exhaust to the turbine 16, e.g., via an exhaust passageway.

The exhaust emission prediction system may include a controller 30connected via communication lines 32 to one or more of the components ofthe engine 10 and the exhaust system 14. For example, the controller 30may receive input via the communication lines 32 from a variety ofsources including, for example, a timer and/or one or more sensorsconfigured to measure temperature, speed, pressure, fuel quantityconsumed, flow rate, amount of reductant injected, and/or otheroperating characteristics of the engine 10 and/or exhaust system 14. Asshown in FIG. 1, the controller 30 may be connected by the communicationlines 32 to a NOx sensor 34 and a humidity sensor 36. The sensors 34, 36may be physical (hardware) sensors. The NOx sensor 34 may be locateddownstream of the engine 10 and the turbine 16, and upstream of theaftertreatment device(s) 20, the injector 22 and the SCR catalyst 24.Alternatively, the NOx sensor 34 may be disposed in other locations inthe exhaust system 14, e.g., downstream from the aftertreatmentdevice(s) 20 and/or the SCR catalyst 24. The humidity sensor 36 may belocated in the air induction system, e.g., at a location upstream of aninlet of the compressor 26, the connection of the EGR passageway 28 tothe air induction system, and/or one or more filters in the airinduction system. Alternatively, the humidity sensor 36 may be disposedat another location that allows the humidity sensor 36 to measure anambient humidity of the atmospheric air.

The controller 30 may include components required to run an applicationsuch as, for example, a computer, a memory module, a secondary storagedevice (e.g., a database), and a processor or microprocessor, such as acentral processing unit, as known in the art. The memory module may beconfigured to store information used by the processor, e.g., computerprograms or code used by the processor to enable the processor toperform functions consistent with disclosed embodiments, e.g., theprocesses described in detail below. The controller 30 may becommunicatively coupled with one or more components of the engine 10and/or the exhaust system 14 to change the operation thereof.Optionally, the controller 30 may be integrated into the engine 10,e.g., as part of an engine control module (ECM). The controller 30 mayuse the inputs to form a control signal based on a pre-set controlalgorithm. The control signal may be transmitted from the controller 30via the communication lines 32 to various actuation devices, such as oneor more components of the engine 10 and/or the exhaust system 14, e.g.,the injector 22 to control the timing and amount of injections.

FIG. 2 is a diagrammatic illustration of the controller 30, according toan exemplary embodiment. The controller 30 may include an empiricalmodel and a physics-based model for determining respective estimationsof an amount of an emissions constituent at a location in the exhaustsystem 14. In the exemplary embodiment, the controller 30 includes anempirical NOx model 40 and a physics-based NOx model 60. The empiricalNOx model 40 and the physics-based NOx model 60 may each include one ormore models, and may be configured to determine respective first andsecond NOx estimations 42 and 62 of an amount of NOx in the flow ofexhaust output from the engine 10. In the exemplary embodiment, theempirical NOx model 40 and the physics-based NOx model 60 are configuredto determine the respective first and second NOx estimations 42 and 62at a location downstream from the turbine 16 and upstream of theaftertreatment device(s) 20. Alternatively, the empirical NOx model 40and the physics-based NOx model 60 may determine the respective firstand second NOx estimations 42 and 62 at other locations downstream fromthe engine 10.

FIG. 3 is a diagrammatic illustration of the empirical NOx model 40 fordetermining the first NOx estimation 42 in the flow of exhaust outputfrom the engine 10, according to an exemplary embodiment. The empiricalNOx model 40 may include one or more models or maps (e.g., a neuralnetwork model and/or map, a curve fitting model and/or map, etc.) thatare data-driven, such as a first NOx model 44 and a first map 46. Thefirst NOx model 44 and/or the first map 46 may be created usingexperimental data from one or more test engines under various operatingconditions. For example, the first NOx model 44 and/or the first map 46may be trained using data for one or more test engines operating undervarious operating conditions, such as, but not limited to, one or morealtitudes (e.g., sea level or a range of altitudes), one or more ambienttemperatures, one or more engine speeds, one or more ambient pressures,and/or one or more ambient humidity levels. Other operating conditionsthat the test engine(s) may be trained under may include one or moreapplication cycles, one or more tasks, one or more aggressiveapplication cycles, one or more non-aggressive application cycles, oneor more steady-state operating conditions, one or more transientoperating conditions, one or more engine configurations (e.g., enginesincluding certain hardware), one or more fuel injection systemcalibrations (e.g., fuel injection timing, amount, or pressure), etc.For operating conditions for which data does not exist (e.g., operatingconditions for which the first NOx model 44 and/or the first map 46 werenot trained), the first NOx model 44 and/or the first map 46 may be usedto predict the performance of the engine 10, for example, byinterpolation and extrapolation.

In the exemplary embodiment, the first NOx model 44 may receive one ormore inputs 48 and determine the first NOx estimation 42 of an amount ofNOx in the flow of exhaust output from the engine 10. The inputs 48 mayinclude one or more of a speed 50 of the engine 10 (e.g., in revolutionsper minute or RPM), a fuel injection timing 51, a fuel injection amount52, a fuel injection pressure 53, a pressure of the intake to the engine10 (e.g., a pressure 54 in the intake manifold 11), a temperature of theintake to the engine 10 (e.g., a temperature 55 in the intake manifold11), and an EGR amount 56. The inputs 48 may be estimated (e.g., usingthe controller 30 and/or virtual sensors) or measured (e.g., usingphysical sensors), as known in the art. Thus, the first NOx model 44 maybe trained to determine the first NOx estimation 42 based on the inputs48 at one or more of the operating conditions described above.

The first NOx model 44 may output the first NOx estimation 42, which maybe input into the first map 46. The first map 46 may also receive one ormore other inputs, such as an ambient humidity 57 measured by thehumidity sensor 36 (FIG. 1). The first map 46 may be used to adjust thefirst NOx estimation 42 based on the ambient humidity 57. For example,the first map 46 may correlate the ambient humidity 57 and a correctionfactor for multiplying with the first NOx estimation 42, and the firstmap 46 may be used to determine the correction factor based on themeasured ambient humidity 57. The controller 30 may multiply the firstNOx estimation 42 by the correction factor to adjust the first NOxestimation 42. Then, the first NOx estimation 42 may be output from theempirical NOx model 40.

FIG. 4 is a diagrammatic illustration of the physics-based NOx model 60for determining the second NOx estimation 62 in the flow of exhaustoutput from the engine 10, according to an exemplary embodiment. Thephysics-based NOx model 60 may include one or more models, such as asecond NOx model 64 and an in-cylinder engine model 66, that may bebased on one or more physical and/or chemical equations for determiningthe performance of the engine 10 (e.g., the second NOx estimation 62 oran estimation of another emissions constituent in the flow of exhaust).For example, the second NOx model 64 and/or the in-cylinder engine model66 may be created using one or more equations governing the functioningof the engine 10, such as the chemical and physical reactions occurringin the cylinders 12, e.g., the reactions governing the combustion of themixture of fuel and the compressed air and/or recirculated exhaust, andother reactions relating to the formation of NOx and/or other emissionsconstituents, etc. In the exemplary embodiment, the second NOx model 64and/or the in-cylinder engine model 66 are not data-driven and thereforeare not trained using experimental data from one or more test engines.

The physics-based NOx model 60 may receive one or more inputs 68. Theinputs 68 may include one or more of the inputs 48 to the empirical NOxmodel 40, such as the engine speed 50, the intake manifold pressure 54,and/or the intake manifold temperature 55. In addition, the inputs 68may include a crank angle 71 of the engine 10 (e.g., in crank angledegrees or CAD), an air flow rate 72 of air into one or more of thecylinders 12, a fuel flow rate 73 of fuel into one or more of thecylinders 12, and/or a volume percent of EGR (EGR_IVC) 74 in one or moreof the cylinders 12 at the closing of the respective intake valve(s).The inputs 68 may be estimated (e.g., using the controller 30 and/orvirtual sensors) or measured (e.g., using physical sensors), as known inthe art. Based on the inputs 68, the physics-based NOx model 60 maydetermine the second NOx estimation 62 for the flow of exhaust outputfrom the engine 10.

In the exemplary embodiment, the in-cylinder engine model 66 may receiveone or more of the inputs 68 to determine one or more outputs 70, whichmay include one or more in-cylinder characteristics of one or more ofthe cylinders 12. The outputs 70 may be determined by the in-cylinderengine model 66 based on one or more equations governing the chemicaland physical reactions occurring in one or more of the cylinders 12. Forexample, the in-cylinder engine model 66 may receive the intake manifoldpressure 54, the intake manifold temperature 55, the air flow rate 72,and/or the fuel flow rate 73. The in-cylinder engine model 66 mayinclude the reactions governing the combustion of the mixture of fueland the compressed air and/or recirculated exhaust. The outputs 70 ofthe in-cylinder engine model 66 may include one or more in-cylindercharacteristics, such as one or more of a gross heat release rate(CylGHRR) 75 of one or more of the cylinders 12 (e.g., in joules percrank angle degrees or J/CAD), a pressure (CylP) 76 of one or more ofthe cylinders 12 (e.g., one or more values of pressure as a function ofthe crank angle for a 720-degree CAD cycle of one of the cylinders 12),a bulk gas temperature (CylT) 77 of one or more of the cylinders 12(e.g., one or more values of bulk gas temperature as a function of thecrank angle for a 720-degree CAD cycle of one of the cylinders 12), abulk gas specific heat (CylCP) 78 of one or more of the cylinders 12(e.g., one or more values of bulk gas specific heat as a function of thecrank angle for a 720-degree CAD cycle of one of the cylinders 12), abulk gas density (CylRho) 79 of one or more of the cylinders 12 (e.g.,one or more values of bulk gas density as a function of the crank anglefor a 720-degree CAD cycle of one of the cylinders 12), a bulk gas mass(CylMass) 80 of one or more of the cylinders 12 (e.g., one or morevalues of bulk gas mass as a function of the crank angle for a720-degree CAD cycle of one of the cylinders 12), and a total heattransfer (CylQ) 81 to the flow of exhaust out of one or more of thecylinders 12 (e.g., one or more values of total heat transfer to theflow of exhaust out of one of the cylinders 12 as a function of thecrank angle for a 720-degree CAD cycle). The outputs 70 from thein-cylinder engine model 66 may be input into the second NOx model 64.

Alternatively, the in-cylinder engine model 66 may be omitted, and oneor more of the outputs 70 of the in-cylinder engine model 66 may bemeasured using one or more physical sensors, such as one or morepressure sensors configured to sense the pressure (e.g., the pressure76) in one or more of the cylinders 12 and/or one or more temperaturesensors configured to sense the temperature (e.g., the bulk gastemperature 77) in one or more of the cylinders 12, to be input into thesecond NOx model 64. The second NOx model 64 and/or one or more virtualsensors may estimate the gross heat release rate 75, the bulk gasspecific heat 78, the bulk gas density 79, the bulk gas mass 80, and/orthe total heat transfer 81 using the inputs received from the physicalsensors. When the in-cylinder engine model 66 is omitted, the second NOxmodel 64 may include the reactions governing the combustion of themixture of fuel and the compressed air and/or recirculated exhaust.

Using either the outputs 70 from the in-cylinder engine model 66 or themeasurements from the physical sensors, in addition to one or more ofthe inputs 68 to the physics-based NOx model 60, the second NOx model 64may determine the second NOx estimation 62 for the flow of exhaustoutput from the engine 10. For example, as shown in FIG. 4, the secondNOx model 64 may receive the engine speed 50, the crank angle 71, theair flow rate 72, the fuel flow rate 73, and/or the volume percent ofEGR 74. The second NOx model 64 may include reactions relating to theformation of NOx and/or other emissions constituents. To determine thesecond NOx estimation 62, the second NOx model 64 may determine otherin-cylinder characteristics, such as an adiabatic flame temperature of astoichiometric mixture of fuel and air along a burn duration from thestart of combustion to the end of combustion and a brake specific fuelconsumption (e.g., in grams per kilowatt-hour or g/kW-hr). The brakespecific fuel consumption may be determined, for example, based on theair flow rate 72 and the fuel flow rate 73. To determine the second NOxestimation 62, the second NOx model 64 may also include othercharacteristics (e.g., characteristics of the fuel), such as astoichiometric air-to-fuel mass ratio of the fuel, a hydrogen-to-carbonratio of the fuel, or a lower heating value (LHV) of the fuel (e.g., inmegajoules per kilogram or MJ/kg).

Referring back to FIG. 2, the empirical NOx model 40 and thephysics-based NOx model 60 may output the respective first and secondNOx estimations 42 and 62 to a NOx determination module 90 in thecontroller 30. The NOx determination module 90 may determine a third NOxestimation 92 of the amount of NOx in the flow of exhaust output fromthe engine 10 (e.g., at a location downstream from the turbine 16 andupstream of the aftertreatment device(s) 20) based on the first NOxestimation 42, the second NOx estimation 62, and the operating conditionof the engine 10, as described in further detail below.

The NOx determination module 90 may output the third NOx estimation 92,which may be input into a second map 94. In the exemplary embodiment,the second map 94 may also receive one or more other inputs, such as ameasured NOx 95 measured by the NOx sensor 34 (FIG. 1). The second map94 may be used to adjust the third NOx estimation 92 based on themeasured NOx 95 to output a final NOx estimation 96, as described below.Alternatively, the second map 94 and the NOx sensor 34 may be omitted,e.g., if there is no NOx sensor 34 present in the exhaust system 14, andthe NOx determination module 90 may determine the third NOx estimation92, which may be the final NOx estimation, without using input from theNOx sensor 34. The controller 30 may be configured to use the final NOxestimation 96 as feedback for controlling the dosing of the reductantusing the injector 22, which may improve the performance of the SCRcatalyst 24.

INDUSTRIAL APPLICABILITY

The disclosed exhaust emission prediction system may be applicable toany exhaust system. The exhaust emission prediction system mayincorporate both the empirical NOx model 40 and the physics-based NOxmodel 60 to provide more accurate predictions for emissionscharacteristics, such as the amount of NOx in the flow of exhaust outputfrom the engine 10.

FIG. 5 shows a flow chart depicting an exemplary embodiment of analgorithm of the software control used in connection with the controller30. The steps described below may be repeated by the controller 30periodically.

The controller 30 may determine the first NOx estimation 42 using theempirical NOx model 40 (step 100). As described above, the empirical NOxmodel 40 includes the first NOx model 44, which may receive one or moreof the inputs 48 and may output the first NOx estimation 42. Thecontroller 30 may adjust the first NOx estimation 42 based on theambient humidity 57 (step 102). As described above, the ambient humidity57 may be measured using the humidity sensor 36. The controller 30 mayuse the first map 46 to determine a correction factor based on theambient humidity 57 and may multiply the first NOx estimation 42 by thecorrection factor. Because the ambient humidity 57 may affect the rateat which NOx is formed, the controller 30 may provide a more accurateestimate of the amount of NOx in the flow of exhaust output from theengine 10 by taking into account the ambient humidity 57.

The controller 30 may also determine the second NOx estimation 62 usingthe physics-based NOx model 60 (step 104). As described above, thein-cylinder engine model 66 may receive one or more of the inputs 68,and may determine one or more in-cylinder characteristics (e.g., thegross heat release rate 75, the pressure 76, the bulk gas temperature77, the bulk gas specific heat 78, the bulk gas density 79, the bulk gasmass 80, and/or the total heat transfer 81), which may be input into thesecond NOx model 64. The second NOx model 64 may receive the in-cylindercharacteristics determined by the in-cylinder engine model 66 and one ormore of the inputs 68, and may output the second NOx estimation 62.Alternatively, as described above, the in-cylinder engine model 66 maybe omitted, and one or more of the in-cylinder characteristics may bemeasured using physical sensors (e.g., pressure and temperature in oneor more of the cylinders 12) and input into the second NOx model 64. Thefirst and second NOx estimations 42 and 62 may be input into the NOxdetermination module 90.

The controller 30 (e.g., the NOx determination module 90) may determinewhether the engine 10 is operating under the operating conditions forwhich the empirical NOx model 40 is trained (the trained operatingconditions) (step 106). The trained operating conditions may include,e.g., one or a range of altitudes, one or a range of ambienttemperatures, one or a range of engine speeds, one or a range of ambientpressures, one or more application cycles, one or more tasks, one ormore aggressive application cycles, one or more non-aggressiveapplication cycles, one or more steady-state operating conditions, oneor more transient operating conditions, one or more engineconfigurations (e.g., engines including certain hardware), one or morefuel injection system calibrations (e.g., fuel injection timing, amount,or pressure), etc.

For example, in an exemplary embodiment, the empirical NOx model 40 maybe trained at approximately sea level and at engine speeds betweenapproximately 800 RPM and approximately 1,800 RPM. It is understood thatthe empirical NOx model 40 may be determined to be trained for a rangeof operating conditions (e.g., engine speeds between approximately 800RPM and approximately 1,800 RPM) even though the data used to train theempirical NOx model 40 may include a subset of operating conditionswithin the range.

The NOx determination module 90 may determine that the engine 10 isoperating under the trained operating conditions (step 106; yes). In theexemplary embodiment described above, for example, the NOx determinationmodule 90 may determine whether the engine 10 is operating atapproximately sea level and at an engine speed between approximately 800RPM and approximately 1,800 RPM. If so, the NOx determination module 90may determine the third NOx estimation 92 based at least in part on thefirst NOx estimation 42 determined by the empirical NOx model 40 (step108). For example, the NOx determination module 90 may determine thatthe third NOx estimation 92 equals the first NOx estimation 42.

The controller 30 may receive the measured NOx 95 from the NOx sensor 34(step 110) and may adjust the third NOx estimation 92 using the measuredNOx 95 (step 112). For example, the second map 94 may be used todetermine a correction factor based on the measured NOx 95. Thecorrection factor may also depend on the operating condition(s) underwhich the engine 10 is performing. For example, the correction factormay adjust the third NOx estimation 92 to be closer to the measured NOx95 when the engine 10 is operating under one or more operationconditions for which there may be relatively more confidence in theaccuracy of the NOx sensor 34, e.g., below a certain period of time ofuse of the NOx sensor 34. The controller 30 may multiply the third NOxestimation 92 by the correction factor to output the final NOxestimation 96. Alternatively, the correction factor may be an offsetthat is added to (or subtracted from) the third NOx estimation 92 tooutput the final NOx estimation 96. Thus, the NOx sensor 34 may be usedto adjust the third NOx estimation 92 to determine the final NOxestimation 96. Thus, the NOx sensor 34 may measure the amount of NOxusing the NOx sensor 34 disposed at or near the same location at whichthe first, second, and third NOx estimations 42, 62, and 92 areestimating the amount of NOx. Alternatively, if the NOx sensor 34 islocated at another location in the exhaust system 14 (e.g., downstreamof the aftertreatment device(s) 20), the second map 94 may be configuredto take into account any differences in the amount of NOx between thelocation of the NOx sensor 34 and the location at which the first,second, and third NOx estimations 42, 62, and 92 are estimating theamount of NOx (e.g., due to the aftertreatment device(s) 20).

Optionally, steps 110 and 112 may be omitted, e.g., if there is no NOxsensor 34 present in the exhaust system 14. As another alternative, orin addition, the controller 30 may use the third NOx estimation 92 todiagnose the NOx sensor 34 (e.g., to determine when the NOx sensor 34fails) or act as a backup virtual NOx sensor if the NOx sensor 34 fails.

Referring back to step 106, the NOx determination module 90 maydetermine that the engine 10 is not operating under the trainedoperating conditions (step 106; no). In the exemplary embodimentdescribed above, for example, the NOx determination module 90 maydetermine that the engine 10 is operating at a relatively high altitudeand/or at an engine speed outside the range of approximately 800 RPM andapproximately 1,800 RPM. If so, then the NOx determination module 90 maydetermine the third NOx estimation 92 based at least in part on thesecond NOx estimation 62 determined by the physics-based NOx model 60(step 114). For example, the NOx determination module 90 may determinethat the third NOx estimation 92 equals the second NOx estimation 62.

Alternatively, the NOx determination module 90 may determine the thirdNOx estimation 92 based on both the first NOx estimation 42 and thesecond NOx estimation 62. According to an exemplary embodiment, the NOxdetermination module 90 may determine a weighing factor that mayindicate a relative weight of the empirical NOx model 40 compared to thephysics-based NOx model 60. The weighing factor may be determined basedon the operating condition(s) under which the engine 10 is performing,and may range from 0 (indicating that the engine 10 is performing underoperating condition(s) for which there is more confidence in theaccuracy of the empirical NOx model 40) to 1 (indicating that the engine10 is performing under operating condition(s) for which there is moreconfidence in the accuracy of the physics-based NOx model 60, e.g., ifthe engine 10 is operating in the domain to which the physics-based NOxmodel 60 applies). The third NOx estimation 92 may be determined byapplying the weighing factor to the first NOx estimation 42 and thesecond NOx estimation 62. If the weighing factor is equal to or closerto 0, then the third NOx estimation 92 may be equal to or closer to thefirst NOx estimation 42 than the second NOx estimation 62. On the otherhand, if the weighing factor is equal to or closer to 1, then the thirdNOx estimation 92 may be equal to or closer to the second NOx estimation62 than the first NOx estimation 42. The weighing factor may vary from 0to 1 in order to correspondingly vary the third NOx estimation 92 fromthe first NOx estimation 42 to the second NOx estimation 62. Afterdetermining the third NOx estimation 92, the controller 30 may receivethe measured NOx 95 from the NOx sensor 34 (step 110) and may adjust thethird NOx estimation 92 using the measured NOx 95 to output the finalNOx estimation 96 (step 112), as described above.

The controller 30 may use the final NOx estimation 96 as feedback forcontrolling the dosing of the reductant using the injector 22. Thecontroller 30 may determine the amount of NOx entering the SCR catalyst24 based on the final NOx estimation 96 and by taking into account theeffect of the other exhaust treatment components (e.g., theaftertreatment device(s) 20) located between the turbine 16 and the SCRcatalyst 24 on the composition of the flow of exhaust. The controller 30may adjust an amount of the reductant injected by the injector 22 basedon the determined amount of NOx entering the SCR catalyst 24 toanticipate and mitigate the release of NOx and/or ammonia downstream ofthe SCR catalyst 24.

The flow chart described above in connection with FIG. 5 depicts anexemplary embodiment of the algorithm and software control. Thoseskilled in the art will recognize that similar algorithms and softwarecontrol may be used without deviating from the scope of the presentdisclosure.

Several advantages over the prior art may be associated with the exhaustemission prediction system. The exhaust emission prediction system mayprovide a hybrid virtual NOx sensing device that includes both theempirical NOx model 40 and the physics-based NOx model 60. Therefore,the exhaust emission prediction system may provide more accurate andreliable estimations of the amount of NOx in the flow of exhaust outputfrom the engine 10. Optionally, the NOx sensor 34 may be omitted, whichmay reduce costs, or the NOx sensor 34 may be diagnosed or correctedusing the estimations determined by the exhaust emission predictionsystem.

The exhaust emission prediction system includes both the empirical NOxmodel 40 and the physics-based NOx model 60, and therefore may provideadvantages from both models 40 and 60. The exhaust emission predictionsystem may determine the final NOx estimation 96 based on whether theengine 10 is operating under trained operating conditions. The empiricalNOx model 40 may have higher accuracy than the physics-based NOx model60 when the engine 10 is operating under trained operating conditions.If the engine 10 is operating under the trained operating conditions,then the final NOx estimation 96 may be determined based at least inpart on the output (e.g., the first NOx estimation 42) from theempirical NOx model 40. Therefore, the exhaust emission predictionsystem may take advantage of the relative accuracy of the empirical NOxmodel 40 under the trained operating conditions.

Because the empirical NOx model 40 may not be as accurate when theengine 10 is not operating under trained operating conditions, the NOxdetermination module 90 may switch to the output (e.g., the second NOxestimation 62) from the physics-based NOx model 60 to determine thefinal NOx estimation 96 or may calibrate the output from the empiricalNOx model 40 using the output from the physics-based NOx model 60 todetermine the final NOx estimation 96. For example, to calibrate theoutput from the empirical NOx model 40, the NOx determination module 90may use the weighing factor to determine how much to weigh the outputsfrom the empirical NOx model 40 and the physics-based NOx model 60. Theweighing factor may favor the empirical NOx model 40 if there is higherconfidence in the empirical NOx model 40 (e.g., based on the operatingconditions of the engine 10), may favor the physics-based NOx model 60if there is higher confidence in the physics-based NOx model 60, or mayaverage the two outputs if neither model 40 or 60 is favored. Therefore,when the engine 10 is not operating under trained operating conditions,the exhaust emission prediction system may take advantage of therelative accuracy of the empirical NOx model 40 and/or the physics-basedNOx model 60, depending on the operating conditions under which theengine 10 is operating.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the disclosed exhaustemission prediction system. Other embodiments will be apparent to thoseskilled in the art from consideration of the specification and practiceof the disclosed exhaust emission prediction system. It is intended thatthe specification and examples be considered as exemplary only, with atrue scope being indicated by the following claims and theirequivalents.

What is claimed is:
 1. An exhaust emission prediction system comprising:an engine configured to generate a flow of exhaust; and a controllerconfigured to: determine a first estimation of an amount of an emissionsconstituent at a first location using an empirical model, the firstlocation being downstream of the engine; determine a second estimationof the amount of the emissions constituent at the first location using aphysics-based model; and determine a third estimation of the amount ofthe emissions constituent at the first location based on at least one ofthe first estimation or the second estimation.
 2. The exhaust emissionprediction system of claim 1, wherein: the empirical model is trainedusing at least one trained operating condition; and the controller isfurther configured to determine an operating condition of the engine anddetermine the third estimation based on whether the at least one trainedoperating condition for the empirical model includes the operatingcondition of the engine.
 3. The exhaust emission prediction system ofclaim 2, wherein: when the at least one trained operating condition forthe empirical model includes the operating condition of the engine, thecontroller is configured to determine the third estimation based atleast in part on the first estimation; and when the at least one trainedoperating condition for the empirical model does not include theoperating condition of the engine, the controller is configured todetermine the third estimation based at least in part on the secondestimation.
 4. The exhaust emission prediction system of claim 3,wherein, when the at least one trained operating condition for theempirical model includes the operating condition of the engine, thecontroller is configured to determine that the third estimation is equalto the first estimation.
 5. The exhaust emission prediction system ofclaim 3, wherein, when the at least one trained operating condition forthe empirical model does not include the operating condition of theengine, the controller is configured to determine that the thirdestimation is equal to the second estimation.
 6. The exhaust emissionprediction system of claim 3, wherein, when the at least one trainedoperating condition for the empirical model does not include theoperating condition of the engine, the controller is configured todetermine the third estimation based on the first estimation and thesecond estimation.
 7. The exhaust emission prediction system of claim 2,wherein the operating condition includes at least one of an altitude, anambient temperature, an engine speed, an ambient pressure, anapplication cycle, an engine configuration, or a fuel injection systemcalibration.
 8. The exhaust emission prediction system of claim 1,wherein the empirical model includes a neural network model or a curvefitting model.
 9. The exhaust emission prediction system of claim 1,wherein, to determine the first estimation, the controller is configuredto input into the empirical model at least one of a speed of the engine,a fuel injection timing of the engine, an amount of fuel injected intothe engine, a pressure of the fuel injected into the engine, a pressurein an intake manifold of the engine, a temperature in the intakemanifold, or an amount of exhaust recirculated into the engine.
 10. Theexhaust emission prediction system of claim 1, wherein the controller isfurther configured to determine at least one in-cylinder characteristicof at least one cylinder of the engine using the physics-based model anddetermine the second estimation using the at least one in-cylindercharacteristic.
 11. The exhaust emission prediction system of claim 10,wherein the at least one in-cylinder characteristic includes at leastone of a pressure, a bulk gas temperature, a bulk gas specific heat, abulk gas density, a bulk gas mass, or a total heat transfer to the flowof exhaust.
 12. The exhaust emission prediction system of claim 1,wherein, to determine the second estimation, the controller isconfigured to input into the physics-based model at least one of a speedof the engine, a crank angle of the engine, an air flow rate into theengine, a fuel flow rate into the engine, a pressure in the engine, atemperature in the engine, or a volume percent of recirculated exhaustgas in the engine.
 13. The exhaust emission prediction system of claim1, wherein the emissions constituent includes NOx.
 14. The exhaustemission prediction system of claim 13, further comprising: at least oneNOx sensor disposed downstream of the engine and configured to output ameasured amount of NOx; wherein the controller is in communication withthe at least one NOx sensor and further configured to adjust the thirdestimation based on the measured amount.
 15. A method of predicting anamount of NOx in a flow of exhaust from an engine using a controller,the method comprising: determining, using the controller, a firstestimation of the amount of NOx at a first location using an empiricalmodel, the first location being downstream of the engine; determining,using the controller, a second estimation of the amount of NOx at thefirst location using a physics-based model; and determining, using thecontroller, a third estimation of the amount of NOx at the firstlocation based on at least one of the first estimation or the secondestimation.
 16. The method of claim 15, further comprising: determiningan ambient humidity; wherein the first estimation is further determined,using the controller, based on the ambient humidity.
 17. The method ofclaim 15, further comprising: determining, using the controller, anoperating condition of the engine; wherein the empirical model istrained using at least one trained operating condition; wherein thethird estimation is further determined, using the controller, based atleast in part on the first estimation when the at least one trainedoperating condition for the empirical model includes the operatingcondition of the engine; and wherein the third estimation is furtherdetermined, using the controller, based at least in part on the secondestimation when the at least one trained operating condition for theempirical model does not include the operating condition of the engine.18. An engine system comprising: an engine configured to generate a flowof exhaust; an injector configured to inject a reductant into the flowof exhaust; a catalytic device configured to receive the flow of exhaustafter being injected with the reductant; a processor; a memory moduleconfigured to store instructions that, when executed, enable theprocessor to: determine a first estimation of an amount of NOx at afirst location using an empirical model, the first location beingdownstream of the engine and upstream of the catalytic device; determinea second estimation of the amount of NOx at the first location using aphysics-based model; determine a third estimation of the amount of NOxat the first location based on at least one of the first estimation orthe second estimation; and adjust an amount of the reductant injected bythe injector based on the determined third estimation.
 19. The enginesystem of claim 18, further comprising a turbine upstream of theinjector and configured to receive the flow of exhaust, wherein thefirst location is downstream of the turbine.
 20. The engine system ofclaim 18, wherein: the memory module is further configured to storeinstructions that, when executed, enable the processor to: determine anoperating condition of the engine; determine the third estimation basedat least in part on the first estimation when the at least one trainedoperating condition for the empirical model includes the operatingcondition of the engine; and determine the third estimation based atleast in part on the second estimation when the at least one trainedoperating condition for the empirical model does not include theoperating condition of the engine.